University of Kurdistan
Dept. of Electrical Engineering
Smart/Micro Grids Research Center
smgrc.uok.ac.ir
A Novel Design of Decentralized LFC to Enhance Frequency Stability of
Egypt Power System Including Wind Farms
G. Magdy, G. Shabib, Adel A. Elbaset, Thongchart Kerdphol, Yaser Qudaih, Hassan Bevrani,
Yasunori Mitani
Published (to be published) in: International Journal on Energy Conversion (I.R.E.CON.), Vol. 6, N. 1
ISSN 2281-5295
(Expected) publication date: March 2019
Citation format for published version:
G. Magdy, G. Shabib, Adel A. Elbaset, Thongchart Kerdphol, Yaser Qudaih, Hassan Bevrani, Yasunori
Mitani. (2019). A Novel Design of Decentralized LFC to Enhance Frequency Stability of Egypt Power System Including Wind Farms. International Journal on Energy Conversion (I.R.E.CON.), Vol. 6, N. 1 ISSN 2281-5295, 13.
Copyright policies:
• Download and print one copy of this material for the purpose of private study or research is permitted. • Permission to further distributing the material for advertising or promotional purposes or use it for any
profitmaking activity or commercial gain, must be obtained from the main publisher.
• If you believe that this document breaches copyright please contact us at [email protected] providing details,
and we will remove access to the work immediately and investigate your claim.
Copyright © Smart/Micro Grids Research Center, 2019
Copyright © 2018 Praise Worthy Prize S.r.l. -
17
International Journal on Energy Conversion (I.R.E.CON.), Vol. 6, N. 1
ISSN 2281-5295 January 2018
A Novel Design of Decentralized LFC to Enhance Frequency Stability
of Egypt Power System Including Wind Farms
G. Magdy1,2, G. Shabib2,3, Adel A. Elbaset4, Thongchart Kerdphol1, Yaser Qudaih5,
Hassan Bevrani6, Yasunori Mitani1
Abstract – This paper presents a real hybrid power system in Egypt, which includes both
conventional generation units and Renewable Energy Sources (RESs) for studying the Load
Frequency Control (LFC) problem. The conventional generation system in the Egyptian Power
System (EPS) is decomposed into three dynamic subsystems; non-reheat, reheat and hydro power
plants. Moreover, real wind speed data extracted from Zafarana location in Egypt is used for
achieving a realistic wind power study. Each subsystem of the EPS has its own characteristics
compared to the others. Moreover, the physical constraints of the governors and turbines such as
Generation Rate Constraints (GRCs) of power plants and speed governor dead band (i.e., backlash)
are taken into consideration. Therefore, this paper proposes a decentralized controller for each
subsystem independently, to guarantee the stability of the overall closed-loop system. Hence, an
optimal PID controller-based Particle Swarm Optimization (PSO) algorithm is proposed for every
subsystem separately to regulate the frequency and track the load demands of the EPS. The
performance of the proposed decentralized controller of each subsystem is compared to the
centralized one under different operational scenarios. The EPS is tested using the nonlinear
simulation by Matlab/SIMULINK. The obtained results reveal the superior robustness of the
proposed decentralized controller against different load disturbance patterns, real wind power
fluctuations and EPS uncertainties. Copyright © 2018 Praise Worthy Prize S.r.l. - All rights
reserved.
Keywords: Load Frequency control (LFC), Decentralized Control, Egyptian Power System, Particle Swarm
Optimization (PSO)
Nomenclature Pn1 Nominal rated Power output for the non-reheat
plant (MW pu)
∆f The frequency deviation of the EPS (Hz) Pn2 Nominal rated Power output for the reheat
D System damping coefficient (pu MW/Hz) plant (MW pu)
H Equivalent inertia constant (pu s) Pn3 Nominal rated Power output for the hydro
T1 Valve time constant of the non-reheat plant (s) plant (MW pu)
T2 Steam valve time constant of the reheat plant ∆Pc1 Regulating the system frequency of the
(s) non-reheat plant (Hz)
T3 Water valve time constant of the hydro plant ∆Pc2 Regulating the system frequency of the reheat
(s) plant (Hz)
G. Magdy et al.
Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved International Journal on Energy Conversion, Vol. 6, N. 1
18
Td Dashpot time constant of the hydro plant speed ∆Pc3 Regulating the system frequency of the hydro
governor (s) plant (Hz)
Th The time constant of the reheat thermal plant VU The maximum limit of the valve gate
(s) VL The minimum limit of the valve gate
Tw Water starting time in hydro intake (s) Number of plants
∆PL Load variation (MW pu) Percentage of rotating load
m The fraction of turbine power (intermediate
Rotating inertia (s/Hz)
pressure section)
Regulating power of plant i
R1 Governor speed regulation non-reheat plant ( ) Number of units in plant i
(Hz/pu MW)
( ) Inertia constant of a unit of plant i (pu MW
R2 Governor speed regulation reheat plant (Hz/pu
s/Hz)
MW)
The current position of particle i at iteration n
R3 Governor speed regulation hydro plant (Hz/pu
n Number of iterations
MW)
pbest of particle i at iteration n
All rights reserved https://doi.org/10.15866/irecon.v6i1.14516
gbest of particle i at iteration n
The velocity of particle i at iteration n
c1, c2 Acceleration constant
w Inertia weight factor
rand () A random number between 0 and 1
TWT Wind Turbine time constant (s)
I. Introduction
The mismatch between electric power generation and
load demand will eventually cause a frequency deviation
as well as tie-line power deviation in the interconnected
power system. Moreover, the large value of frequency
deviation will cause many problems such as damaging the
equipment, transmission line overloading and interference
with system protection [1]. On the other hand, utilizing
Renewable Energy Sources (RESs) is attracting great
attention as a solution to future energy shortages. However,
the irregular nature of RESs and random load deviations
cause many control problems. Moreover, if the penetration
level of RESs is high, the system will suffer from reducing
its inertia. This may lead to increase the system frequency
deviation. Therefore, frequency control may be difficult in
case of mismatch between electric power generation and
load demand particularly with the growing penetration of
RESs (e.g., wind and solar energy), which are integrated
into power systems [2]. To solve such problem, Load
Frequency Control (LFC) issue is introduced to maintain
the system frequency at nominal value. This issue is
considered one of the most important control strategies of
power system operations, since it maintains system
frequency and power variations at their standard values.
The system frequency depends on active power, while
voltage greatly depends on reactive power. Therefore, the
control of power systems can be classified into two
fundamental issues: a) control of the active power along
G. Magdy et al.
Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved International Journal on Energy Conversion, Vol. 6, N. 1
19
with frequency (i.e., LFC) and b) control of reactive power
along with the regulation of voltage [3].
According to the most recent research, several control
strategies were implemented in the LFC loops of different
power systems. Sun et al. [4] used a robust H∞ sliding
mode control for the frequency stability enhancement of
interconnected power systems. Yousef et al. [5] used an
adaptive fuzzy logic approach-based LFC in multi-area
interconnected power systems. Sayed et al. [6] used
Linear Quadratic Regulator (LQR) with Kalman filter for
automatic generation control in a restricted multi-area
power system. Although the aforementioned control
strategies gave a good dynamic response, they depend on
designer's experience. Raheel et al. [7] tested the
robustness of the Coefficient Diagram Method (CDM)
controller beside the effect of Electric Vehicles (EV) in
his control strategy for a small power system. Moreover,
Princess et al. [8] studied the same control strategy in [7]
for the modern power system. Tarek et al. [9] presented a
combination of CDM and Ecological Optimal Technique
(EOT) for LFC of the multi-area power system. However,
the structure of the control technique in [7]-[9] is
complicated, as it requires more steps to obtain its
parameters. Kunya et al. [10] used Model Predictive
Control (MPC) based LFC for the large interconnected
power system. The predictive control strategy in [10] has
the advantages of fast response, simple structure and easy
handle system constraints and nonlinearities. However, it
takes more time for the online calculations at each
sampling time. On the other hand, most industrial
applications are performed based on the
Proportional-Integral-Derivative (PID) controller due to its
many merits, among which: economic cost, simplicity for
parameters tuning, robustness and a successful practical
controller, which can provide excellent control
performance regardless of the perturbations and variations
in the system parameters [11]. However, the PID controller
suffers from a complicated process of parameters tuning
based on the trial and error method. In such a case, the
robustness of the system is not guaranteed against further
perturbations in its parameters. Therefore, several
optimization algorithms were used for finding the optimal
parameters of the PID controller, such as Particle Swarm
Optimization (PSO) [12], cuckoo optimization algorithm
[13], ant colony Algorithm [14], etc [30].
According to the previous studies on the LFC issue,
interconnected power systems were studied, such as
thermal power plants (e.g., reheat and non-reheat turbines)
and/or hydropower plants depending on the number of
areas. However, most of the existing realistic power
systems comprise multi-source dynamics generators:
thermal, hydro, and gas power plants. Therefore, several
types of power plants should be added to the LFC problem
to achieve realistic studies as reported in this research. This
requires using a decentralized control strategy, since the
dynamic response of each type of power plant is different
from the others. Furthermore, the centralized control
scheme is implemented with difficulty due to the excessive
cost of transmitting data over the long distances as well as
the errors, which might be caused accordingly [15]. Hence,
the decentralized scheme is more accurate and realistic
than the centralized control strategy. On the other hand, the
model of the power system studied in [9]-[13] is a simple
and linear system depending only on conventional
generators. However, several RESs should be integrated
into the interconnected power system to achieve more
realistic study. Therefore, a few studies have faced the
challenge of the integration of RESs into power systems by
using different LFC control strategies. Hasanien et al. [16]
presented a symbiotic organisms search algorithm for
obtaining the optimal parameters of the frequency
controller in the interconnected power system including
wind farms. Hasanien [17] used the whale optimization
algorithm for obtaining the optimal PID controller
parameters in an interconnected modern power system
including renewable sources.
Nowadays, RESs such as wind farms and solar power
plants are attracting great attention as a solution for future
energy shortages. Therefore, the modern power system is
facing many challenges [18], [19]. According to the Global
Wind Energy Council (GWEC), the installed wind power
reached 52.5 GW in 2017. Moreover, the total installed
wind power worldwide reached 539.5 GW at the end of
2017 [20], while the total Photovoltaics (PV) installation in
Europe reached up to 156 GW in 2018, based on the global
market outlook for photovoltaics [21]. Therefore, wind
energy is the fastest growing and the most widely utilized
in modern power systems as it has a lower installation cost
compared to the photovoltaic system. Thus, wind energy
represents a significantly larger portion of the installed
electrical power from renewable energy. Hence, this
research focuses on the effect of the integration of wind
energy into the EPS on system frequency stability.
Moreover, for achieving more realistic study, this research
presents a real hybrid power system (i.e., The EPS)
containing both conventional power plants and RESs (i.e.,
wind energy) for facing the frequency stability issue, while
wind speed data are extracted from Zafarana location in
Egypt [16].
Based on the above analysis, the main contribution of
this work includes the following aspects: (i) it presents a
real multi-source power system in Egypt, which consists
of three dynamic subsystems (i.e., non-reheat, reheat, and
hydro power plants) as well as the integration of wind
energy for studying the LFC problem, in addition:
inherent nonlinearities, speed governor dead band (i.e.,
backlash) and GRCs of each power plants are taken into
consideration. (ii) the authors propose a decentralized
control strategy as each subsystem in the EPS has its own
characteristics compared to the others. Each subsystem in
the EPS is considered as Single-Input Multi-Output
(SIMO) and a decentralized controller is proposed for
each subsystem independently. The proposed
decentralized model is developed for the following
G. Magdy et al.
Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved International Journal on Energy Conversion, Vol. 6, N. 1
20
problems: a) unexpected external disturbances, b) non-
linearity in the interactions, and c) system parameter
variations due to changes in the operating conditions. (iii)
the proposed decentralized frequency control is based on
a robust PID controller, which is optimally designed by
the Particle Swarm Optimization (PSO) algorithm.
Finally (iv) a comparison between the proposed
decentralized and the centralized model is studied to
detect the merits and demerits of each model design.
The rest of this paper is arranged as follows: Section 2
describes the configuration and state equations of the
EPS. The proposed control methodology for the studied
system is presented in Section 3. Section 4 presents the
simulation study. The simulation results-based on time
demine are discussed in section 5. Finally, the conclusion
is given in Section 6.
II. Power System Overview and Modeling
II.1. Dynamic Model of the EPS and State Equations
The power system presented in this study is a real
power system in Egypt. It is divided into seven strongly
tied zones: Cairo, Middle Egypt, Upper Egypt, East El-
Delta, El-Canal, West El-Delta and Alexandria, as shown
in Fig. 1. Each zone comprises several power plants (non-
reheat, reheat, and hydro power plants or a combination
of them). The EPS has more than 180 power plants,
moreover it is classified into 3 categories: a) Non-reheat
power plants represented by gas turbine power plants and
a little number of steam power plants. b) Reheat power
plants, mainly represented by thermal power plants or
combined cycle power plants. c) Hydropower plants
contribute for almost 14% of the installed capacity. In
recent times, the EPS is combining several RESs such as
wind turbines and solar power plants, which contribute by
almost 3% of the installed capacity. However, in the
future, the Egyptian Electricity Holding Company
(EEHC) aims to increase the electric energy demand from
RESs. The total generation capacity and peak loads are
35,220 MW and 28,015 MW, respectively, according to
the annual report of the Egyptian Electricity Holding
Company in 2015 [22]. The National Energy Control
Center (NECC) in Egypt has implemented an advanced
dynamic model of LFC for the EPS, as shown in [22].
Moreover, this model was rebuilt using Matlab/Simulink,
with some manipulation, as reported in [23].
In this research, the studied power system (i.e., the EPS),
which consists of seven strongly tied zones, is carried out
based on the NECC. Moreover, interconnection details are
not considered. Therefore, the EPS is represented by a
single area power system model. The studied power system
comprises steam power plants (reheat and non-reheat
turbine), gas power stations (non-reheat turbine),
hydropower plants and wind farms. The nonlinear model
of the EPS considering wind energy with decentralized
controllers is represented in Fig. 2. The three subsystems
(i.e., non-reheat, reheat and hydro power plants) are given
for the EPS with inherent nonlinearities, i.e., a speed
governor dead band (backlash) and GRCs of power plants.
Backlash is defined as the total magnitude of sustained
speed change where the maximum and minimum limits
(VU, VL) restrict valve opening/closure, while the GRC
limits the generation rate of output power, which is given
as 0.2 pu MW/min and 0.1 pu MW/min for non-reheat and
reheat turbines, respectively. However, the actual GRC of
a hydropower plant is about 0.5 pu MW/min, which is
higher than the generation rate corresponding to any
practical disturbance and hence it will be neglected [1].
Fig. 1. Typical single-line diagram of Egyptian power system
The EPS parameters that are independent from the
system operation are given in Table I. However, the
other parameters vary with time depending on the
operating conditions. The nominal parameters values
are given in Table II.
The dynamic equations of the EPS in Fig. 2 can be
derived and written in the state variable form as follows:
= + + (1)
= + + (2)
Hence, the complete state-space equations for the EPS
can be obtained as in (3).
From the nonlinear model of the EPS in Fig. 2, there
are two inputs (∆PL and ∆PC), the output is the
frequency deviation ∆f in the linearized system,
considering ∆PL as load disturbance input in this
research. Using the data given above, the transfer
function of the EPS model G (s) given by Fig. 2 and
the equations of state space
⎡ − 0
National Energy Control Center in Egypt
8000 MW 4695 MW
Cairo ( Zone 1 )
Alexandria ( Zone 7 )
4590 MW
West Delta ( Zone 6 )
4365 MW
4150 MW Middle Egypt
( Zone 2 ) 2800 MW Upper Egypt
( Zone 3 )
5860 MW East Delta ( Zone 4 )
El - Canal ( Zone 5 )
G. Magdy et al.
Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved International Journal on Energy Conversion, Vol. 6, N. 1
21
⎢ 2 ⎢ − − 1
0 0 0
⎢ ⎢ 1
⎢ − 0 − 0
= ⎢ 1 ⎢ − 0 0 −
0 ⎢ 2
⎢
⎢(−2 + 2 ) 2 2 0 2 −
⎢
⎢ ( − ) − − 0 −
⎣
∆
⎡
∆
⎢
⎢
∆
= [ 1 0 0 0 0 0 ] ⎢∆
⎢
⎢
∆
⎣
∆
representation given by Eqs. (1) and (2) can be calculated
using the MATLAB function “SS2TF” in the signal
processing toolbox and are given by Eq. (4).
TABLE I
SYSTEM PARAMETERS INDEPENDENT FROM THE SYSTEM OPERATION
Parameter Value Parameter Value
D 0.028 R2 2.5
T1 0.4 R3 1.0 T2 0.4 VU, Non-reheat 0.05
T3 90 VL, Non-reheat -0.05 Td 5 VU, Reheat 0.05 Th 6 VL, Reheat -0.05 Tw 1.0 VU, Hydro 0.01 m 0.5 VL, Hydro -0.01 R1 2.5 GRC1 0.2
GRC2 0.1 TWT 0.3
TABLE II SYSTEM PARAMETERS INDEPENDENT FROM THE SYSTEM OPERATION
Parameter H Pn1 Pn2 Pn3
Value 5.7096 0.2529 0.6107 0.1364
0 ⎤
⎥
0
⎡
⎤
0 ⎥ ⎢ −
⎥
⎥ ∆ ⎢
⎥⎡− 1 ⎤
⎡∆ ⎤ −⎥
0 ⎥⎥⎥⎢⎢∆ ⎥⎥ + ⎢⎢⎢⎥⎥ [∆ ] + ⎢⎢⎢
002 ⎥⎥⎥ [∆ ]
∆
0 ⎥⎢∆ ⎥⎥
⎢⎢⎥⎥⎢⎢−20 ⎥⎥
⎥⎢
2 2 ⎥⎣∆ ⎦ ⎢ ⎥
⎣ ⎦
⎥
⎢⎥ (3)
⎥⎥ ⎢⎣ − ⎥⎦
The state variables of matrices are:
= , = , = ,
= , = − , =
(4)
( ) =
II.2. Wind Power Plant termed by equations in [17].
This research uses GAMESA wind turbine, which is
The mechanical power from the wind turbine model installed at Zafarana location in Egypt [24]. The details of
can be written as the following equations [17]: this wind turbine are given in the Appendix. Moreover,
real wind speed data, which was =( , ) (5) extracted from Zafarana location during one day this research uses
[16]. In that case, the rated wind speed was 16 m/s.
where ρ is air density (kg/m3), AT is the rotor swept area In this study, the EPS includes an aggregated model of
⎦
⎤
⎥
⎥
⎥ + [0][∆
⎥
⎥
⎦
] + [0][∆ ]
G. Magdy et al.
Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved International Journal on Energy Conversion, Vol. 6, N. 1
22
(m2), VW is the rated wind speed (m/s) and CP represents the wind farm, i.e.1200 wind turbine units of 850 kW each beside
the conventional generation units.
the power coefficient of the rotor blades, which can be
Fig. 2. A nonlinear model of the EPS for seven strongly tied zones considering a wind farm with decentralized controllers
The wind generator is modeled as a first-order transfer
function of a unity gain and wind time constant (TWT).
Therefore, the real output wind power from the wind farm
is shown in Fig. 3.
Fig. 3. Dynamic response of real power of the wind farm.
III. Control Methodology
III.1. Conventional PID Controller
In this research, the proposed decentralized control
strategy for frequency control is based on the PID
controller, which is one of the earliest industrial
controllers. The PID controller is composed of three terms
(i.e., gains), which are proportional gain Kp, integral gain
Ki, and derivative gain Kd. Its transfer function is expressed
as follows:
( ) = + + (6)
According to the suffering of the PID controller for
finding their optimal parameters, this research uses an
intelligent searching method: PSO. It is simple and fast
searching intelligent technique modeled to tune the
parameters of the PID controller. The main objective of the
PSO algorithm is to minimize the system frequency
deviation by obtaining the optimal PID controller
parameters. Moreover, the integral of squared-error (ISE)
is used in this study as an objective function of the
optimization technique [17].
III.2. Particle Swarm Optimization
The PSO was first introduced by Kennedy and Eberhart
in 1995 [25]. It is a global optimization algorithm based on
the evolutionary computation technique. The basic
operation principle of PSO is developed on a swarm of fish
schooling, birds flocking etc. Birds are either dispersed or
go together from one place to another for searching their
food. Furthermore, one of them can discover the place
where food can be found due to the information
G. Magdy et al.
Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved International Journal on Energy Conversion, Vol. 6, N. 1
23
transmitted by other birds at any time while searching food
[26].
In the PSO algorithm, instead of using evolutionary
operators, individuals called particles are used. Therefore,
a swarm consists of several particles and each particle
represents a potential to the problem. Each particle in PSO
flies in the search space according to its own flying
experience and its companion flying experience. Each
particle is treated as a particle in D-dimension search
space. The particle position is represented as Xi and the
best previous position of any particle is recorded; this
value is called pbest. Another value that is tracked by a
global version of the PSO, which is the overall best value
called gbest [27]. The velocity of particle i is represented
by Vi and all particles are updated according to the
following equations:
= ∙ + ∙ ( ) ∙ ( − )
(7)
+ ∙ () ∙ −
= + (8)
Eqs. (7) and (8) are used to calculate the new velocity
and new position of each particle according to its previous
values. In this study, PID controller tuning is considered as
an optimization problem handled by the PSO algorithm.
Here, each particle in the search space introduced a
probable solution for PID gains (Kp, Ki, Kd), which are a 3-
dimensional problem. The performance of the probable
solution point is determined by a fitness function which
consisting of several component functions. These
components are a steady-state error (Ess), peak overshoot
(Mp), rise time (Tr) and settling time (Ts). The selection of
PSO parameters decides to a great extent the ability of
global minimization. Therefore, according to the trials, the
PSO algorithm parameters are given in Table III, which are
used for verifying the performance of PSO-PID. In this
research, the intelligent based PID controller has been
applied to the LFC issue of the EPS considering wind
energy using PSO algorithm as decentralized and
centralized control schemes.
TABLE III
PSO PARAMETERS
Parameter n c1 c2 w
Value 50 0.12 1.2 0.9
The EPS has three dynamic subsystems: non-reheat,
reheat, and hydro power plants. In addition, wind energy
is integrated into the EPS. The response of each
subsystem is different from each other. Therefore, the
optimal decentralized PID controller based on PSO
algorithm has been designed for every subsystem
separately to regulate the frequency and to track the load.
The obtained values of decentralized PID controllers’
parameters based on the PSO technique are given in Table
IV.
TABLE IV
DECENTRALIZED PID CONTROLLERS’ PARAMETERS FOR EVERY SUBSYSTEM
Type of subsystem PID Parameter
Kp Ki Kd
Non-Reheat power
plant 26.5370 16.3125 -0.5080
Reheat power plant 9.68204 0.806941 18.73075 Hydro power plant 38.5370 18.1430 0.1
The optimal centralized PID controller-based PSO is
used to make a fair comparison with the optimal
decentralized model for stabilizing EPS frequency
considering wind energy. Moreover, the main idea behind
combining the three controllers in one centralized
controller is to simplify the optimization process and to
compare the performance of both schemes. The obtained
values of the centralized PID controllers based on PSO
technique are given in Table V.
TABLE V
CENTRALIZED PID CONTROLLERS’ PARAMETERS FOR THE OVERALL SYSTEM
Overall EPS PID Parameter
Kp Ki Kd
Centralized Controller 71.2532 5.9055 6.10758
IV. Simulation Study
The designed controllers based on PSO algorithm are
applied to regulate the system frequency of the EPS, which
comprises three dynamic subsystems; non-reheat, reheat,
and hydro power plants, as well as wind energy. In this
research, the decentralized LFC scheme is compared to the
centralized one for the EPS and considering the effect of
system non-linearity. The simulation results and analysis
are carried out using Matlab/Simulink software®, which
took into account the GRCs of different generation sources
into account. A literature survey shows that many
researches in the area of LFC problem assume that load
disturbance is a step change disturbance, which represents
the forced outage of a generation unit or the sudden switch
off of a massive load. However, in fact, the load
disturbances are complex and have random nature.
Therefore, different patterns of load disturbances, wind
power fluctuations and system parameters variation (i.e.,
system uncertainty) are applied to the proposed control
schemes of the Egyptian LFC to validate that the
centralized design is more robust in case of low
disturbance. However, the decentralized control scheme
gives a reliable performance under large variation in load
conditions and wind power uncertainties.
G. Magdy et al.
Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved International Journal on Energy Conversion, Vol. 6, N. 1
24
V. Simulation Results and Discussion
The detailed model of the EPS considering wind energy
is built using Matlab/ Simulink model. Moreover, the code
of the PSO algorithm (i.e., M-file) is interfaced with the
EPS model to perform the optimization process. The
dynamic response of the studied power system with the
control schemes is obtained and evaluated under different
operating conditions through the following scenarios.
V.1. Scenario 1: Conventional Load Patterns
In this scenario, different conventional load patterns are
applied to the EPS without the wind energy using the two
proposed control schemes, which are based on PSO
algorithm. The EPS performance with the proposed
decentralized controllers based on PSO algorithm is tested
and compared with the system performance with a
centralized controller based on the same optimization
technique. This comparison is evaluated under different
load patterns as the follows:
Case A: in this case, the EPS with the proposed
control schemes are tested by implementation different
patterns of step load disturbances. The dynamic
responses of each subsystem (i.e., non-reheat, reheat
and hydro power plant) in case of nominal system
parameters are shown in Fig. 3. It is clear that the
dynamic response of each subsystem is different than
the others.
Therefore, the authors propose the modern design of
a
decentralized controller for each subsystem
independently to guarantee the stability of the overall
closed-loop system. Fig. 4 shows the dynamic response
of the EPS for the studied cases 1 and 2. It has been
noticed that the model of decentralized controllers
based on PSO algorithm is more robust and has a fast
response to all disturbance cases compared to the
centralized model. Moreover, the centralized controller
design cannot handle large disturbance. The
performance specifications, maximum overshoot
(MOS), maximum undershoot (MUS) and maximum
settling time (TS) of the studied system with the
proposed control schemes of case A during the whole
period of simulation (5 minutes) have been compared in
Table VI.
G. Magdy et al.
Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved International Journal on Energy Conversion, Vol. 6, N. 1
25
Fig. 3. The response of different Power plants for LFC problem of the EPS for case no.1 of Scenario A
Fig. 4. The frequency deviation of the EPS of Scenario 1-Case A
G. Magdy et al.
Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved International Journal on Energy Conversion, Vol. 6, N. 1
26
TABLE VI PERFORMANCE SPECIFICATION OF THE EPS FOR SCENARIO 1, CASE A
Case A Decentralized Centralized Control strategy Control strategy
MUS (pu) MOS (pu)
0.01403 0.01410
0.01277 0.01280
TS (s) 17.412 43.585
MUS (pu) MOS (pu)
0.02360 0.01381
0.1020 0.06823
TS (s) 20.182 -
Case B: the comparison between the two-model is
represented by using a series of step load changes with
random magnitudes to test the robustness and effectiveness
of the proposed decentralized controllers.
From Fig. 5, it is clear that the dynamic response of the
centralized control design oscillates to such an extent that
it is not satisfactory. In contrast, it gives a satisfactory
performance in case of applying slight load change.
Although the proposed decentralized control model gives
a little larger overshoot than the centralized control model
in some cases, it gives robust stability and has faster
settling time than the centralized model. The performance
specifications, i.e. MOS, MUS and TS of the studied system
with proposed control schemes of case B during the whole
period of simulation (5 minutes) have been compared in
Table VII.
Fig. 5. The frequency deviation of the EPS of Scenario 1-Case B
G. Magdy et al.
Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved International Journal on Energy Conversion, Vol. 6,
N. 1
27
Fig. 6. The frequency deviation of the EPS of Scenario 1-Case C
Fig. 7. The frequency deviation of the EPS of Scenario 1-Case D
G. Magdy et al.
Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved International Journal on Energy Conversion, Vol. 6,
N. 1
28
Fig. 8. The frequency deviation of the EPS of Scenario 2
TABLE VII
PERFORMANCE SPECIFICATION OF THE EPS FOR SCENARIO 1, CASE B
Case B Decentralized Centralized Control strategy Control strategy
MUS (pu) MOS (pu)
0.005424 0.01001
0.003854 0.003036
TS (s) 13.701 36.212
MUS (pu) MOS (pu)
0.0111 0.01408
0.010913 0.01285
TS (s) 16.576 37.076
Case C: In this case, the ramping of load change for
different patterns have been applied to the EPS to evaluate
the performance of two-control schemes. The simulation
results concluded that favorable results have been obtained
from the proposed decentralized control scheme as seen in
Fig. 6, while the dynamic response of the centralized
model oscillates to such an extent that it is not acceptable.
The performance specifications, i.e. MOS, MUS, and TS of
the studied system with proposed control schemes of case
C during the whole period of simulation (5 minutes) have
been compared in Table VIII.
TABLE VIII
PERFORMANCE SPECIFICATION OF THE EPS FOR SCENARIO 1, CASE C
Case C Decentralized Centralized Control strategy Control strategy
MUS (pu) MOS (pu)
0.003181 0.01396
0.01018 0.01268
TS (s) 16.644 42.925
MUS (pu) MOS (pu)
0.02379 0.02282
0.8605 0.2923
TS (s) 28.189 -
Case D: in this scenario, the influence of system
parameters variations (i.e., system uncertainty) is
studied to validate the effectiveness of the proposed
control scheme. Therefore, the EPS is tested under a
new operation condition, as reported in Table IX. Here,
the equivalent inertia constant of the overall system is
calculated as in Eq. (9) [28]:
(9)
In this case, the EPS with the proposed two-model
has been tested for three different load patterns as seen
in Fig. 7. It is clear that the proposed model of
decentralized controllers based on PSO algorithm
achieved a robust stability against system uncertainties,
while the system response with the centralized control
model oscillates violently, in contrast, it gives a
satisfactory response to small disturbances. The
performance specifications of the proposed two-control
model for this case have been compared in Table X.
TABLE IX
TWO OPERATION CONDITIONS OF THE EPS
Parameter H Pn1 Pn2 Pn3
Case D 6.1452 0.3335 0.5455 0.1210
Base value 5.7096 0.2529 0.6107 0.1364
V.2. Scenario 2: Realistic/Modern
Load Pattern for Short-Term Study
G. Magdy et al.
Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved International Journal on Energy Conversion, Vol. 6, N. 1
29
In this scenario, The EPS with the proposed control
schemes is tested and evaluated under a realistic load
pattern for short-term study (i.e., 15 minutes). The
combination of both step load change, which represents
the forced outage of the generation unit or the sudden
switch offload, and ramp load change, which represents
industrial load produces a random change in the load,
creates a realistic load pattern.
TABLE X
PERFORMANCE SPECIFICATION OF THE EPS FOR SCENARIO 1, CASE D
Case D Decentralized Centralized Control strategy Control strategy
MUS (pu) MOS (pu)
0.004756 0.002797
0.003519 0.001612
TS (s) 15.982 44.684
MUS (pu) MOS (pu)
0.005434 0.01279
0.2181 0.3816
TS (s) 18.764 -
MUS (pu) MOS (pu)
0.009987 0.01289
0.578 2.576
TS (s) 16.461 -
Therefore, realist load is considered as a combination of
high random load change (i.e., industrial load), medium
random load change (i.e., official load) and low random
load change (i.e., Residential load). Fig. 8 shows that the
frequency response of the EPS is affected by the random
changes of a realistic load, while the dynamic response of
the proposed decentralized control model gives a reliable
performance in terms of maintaining the frequency and
yields small frequency transient compared to the
centralized control model. The performance specifications
like MOS, MUS, and TS of the EPS with the proposed
control schemes of this scenario during short-term
simulation study (15 minutes) have been compared in
Table XI.
TABLE XI
PERFORMANCE SPECIFICATION OF THE EPS FOR SCENARIO 2.
Scenario 2 Decentralized
Control strategy Centralized
Control strategy
MUS (pu) 0.03648 0.0359 MOS (pu) 0.01873 0.0295
TS (s) 32.5 41.715
V.3. Scenario 3: Realist/Modern Load and Wind Power
Patterns for Long-Term Study
In this scenario, the effectiveness and robustness of the
proposed decentralized scheme are evaluated by
implementing a realistic load (i.e., industrial, official and
residential loads) and wind power patterns for long-term
study (i.e., 24 hours). The real wind speed data extracted
from Zafarana location in Egypt is used as reported in
[16]. Moreover, the real wind power output from an
aggregated model of the wind farm is illustrated in Fig. 3.
It is clear that the real wind power randomly fluctuates
due to the nature of wind speed in Zafarana location.
Fig. 9 shows the frequency deviation of the EPS with
the control schemes under the effect of realistic load and
wind power pattern for full one day. According to the
European network of transmission system operators for
electricity codes [29], the standard frequency range is
±0.2 Hz, the maximum instantaneous frequency deviation
is
0.8 Hz, the maximum steady-state frequency deviation is
0.5 Hz and time to recover frequency is 1 min for the LFC
of the power system of a 50 Hz nominal frequency.
Although the decentralized control model gives some
Fig. 9. The frequency deviation of the EPS of Scenario 3
G. Magdy et al.
Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved International Journal on Energy Conversion, Vol. 6, N. 1
30
minor fluctuations, it is within acceptable ranges
compared to the centralized control model. The system
performance specifications of this scenario have been
compared in Table XII.
TABLE XII
PERFORMANCE SPECIFICATION OF THE EPS FOR SCENARIO 3
Scenario 3 Decentralized
Control strategy Centralized
Control strategy
MUS (pu) 0.0426 0.04108 MOS (pu) 0.02628 0.04228
TS (s) 46.876 82.6920
VI. Conclusion
This research has presented a decentralized LFC
design for the EPS using an optimal PID controller-based
PSO algorithm. The EPS includes both conventional
generation units, which are decomposed into three
dynamic subsystems (i.e., non-reheat, reheat and hydro
power plants) and wind energy, which is extracted from
Zafarana wind farm located at the edge of the Red Sea in
Egypt. The performance of the proposed decentralized
LFC model (i.e., subsystem controller) is tested and
compared to the centralized model under variation in
loading patterns, loading conditions, system parameters
and wind farm penetration. The EPS with the proposed
control schemes has been tested and evaluated by using
Matlab/SIMULINK software®. The simulation results
have shown that the dynamic response of the EPS with
the proposed decentralized control strategy is faster and
better damped than when using the centralized control
strategy. On the other hand, the centralized controller
gives a satisfactory performance respect to the proposed
decentralized model in case of applying a slight load
change. In contrast, it oscillates to such an extent that it is
not satisfactory in case of applying large load
disturbance.
Appendix
Wind Turbine model: manufacturer: GAMESA (Spain),
model: G52/850, rated power: 850 kW, rotor diameter: 52
m, swept area: 2.124 m2, Cut-in wind speed 4 m/s, Rated
wind speed 16 m/s, Cut-off wind speed 25 m/s, maximum
generator output speed 1900 rpm, and output voltage 690
V.
References [1] H. Bevrani, Robust Power System Frequency Control (New York,
Springer, 2nd edition, 2014). [2] T. Kerdphol, F. S. Rahman, Y. Mitani, k. Hongesombut, S.
Kufeoglu, Virtual Inertia Control-Based Model Predictive Control
for Microgrid Frequency Stabilization Considering High
Renewable Energy Integration, Sustainability, vol. 9 n. 5, 2017, pp.
1-21. [3] P. Kundur, Power System Stability and Control (New Delhi: Tata
McGraw-Hill Education, 5th reprint, 2008). [4] Y. Sun, Y. Wang, Z. Wei, G. Sun, X. Wu, Robust H load frequency
control of multi-area power system with time delay: A sliding mode
control approach, IEEE/CAA Journal of Automatica Sinica, vol. 5
n. 2, 2018, pp. 1-8.
[5] H. Yousef, Adaptive fuzzy logic load frequency control of multi-
area power system, Electrical power and Energy Systems, vol. 68
n. 1, 2015, pp. 384-395. [6] S. H. Shahalami, D. Farsi, Analysis of Load Frequency Control in
a restructured multi-area power system with the Kalman filter and
the LQR controller, International Journal of Electronics and
Communications, vol. 86 n. 1, 2018, pp. 25-46. [7] Raheel Ali, Tarek Hasan Mohamed, Yaser Soliman Qudaih, Y.
Mitani, A new load frequency control approach in an isolated small
power system using coefficient diagram method, International
Journal Electrical Power and Energy Systems vol. 56 n. 1, 2014,
pp. 110-116. [8] P. Garasi, Y. Qudaih, R. Ali, M. Watanabe, Y. Mitani, Coefficient
Diagram Method Based Load Frequency Control for a Modern
Power System, Journal of Electronic Science and Technology, vol.
12 n. 3, 2014, pp. 270-276. [9] T.H. Mohamed, G. Shabib, H. Ali, Distributed load frequency
control in an interconnected power system using ecological
technique and coefficient diagram method, International Journal
Electrical Power and Energy Systems vol. 82 n. 1, pp. 110-116,
2016. [10] A.B. Kunya, M. Argin, Model Predictive Load Frequency Control
of Multi-Area Interconnected Power System, IEEE Conf., Texas
Power, and Energy Conference (TPEC)~College Station~
February 8-9, 2018, TX, USA. [11] G. Shabib, E.H. Abd-Elhamed, G. Magdy, Robust Digital Redesign
of Continuous PID Controller for Power System Using Plant-Input-
Mapping, WSEAS TRANSACTIONS on POWER SYSTEMS, vol. 13 n. 1, 2018, pp. 31-39.
[12] H. Illias, A. Fikri, M. Zahari, H. Mokhlis, Optimisation of PID
controller for load frequency controller in two-area power system
using evolutionary particle swarm optimisation, International
Journal Electrical Systems vol. 12 n. 2, 2016, pp. 315-324. [13] M. Gheisarnejad, An effective hybrid harmony search and cuckoo
optimization algorithm based fuzzy PID controller for load
frequency control, Applied Soft Computing, vol. 65 n. 1, 2018, pp.
121-138. [14] G. El-Saddy, E.A. Ibrahim, A.A. Donkol, Ant Colony PID
Controllers for Nonlinear Load Frequency Control System,
International Journal on Power Engineering and Energy (IJPEE),
vol. 8 n. 1, 2017, pp. 719-724. [15] S. saxena, Y.V. Hote, Decentralized PID Load Frequency Control
for perturbed multi-area power systems, International Journal of
Electrical Power & Energy Systems, vol. 81 n. 1, 2016, pp. 405-
415. [16] H.M. Hasanien, A.A. El-fergany, Symbiotic organisms search
algorithm for automatic generation control of interconnected power
systems including wind farms, IET Gener. Transm. Distrib., vol. 11 n. 7, 2017, pp. 1692–1700.
[17] H.M. Hasanien, whale optimization algorithm for obtaining the
optimal PID controller parameters in interconnected modern power
system including renewable sources, IET Gener. Transm. Distrib.,
vol. 12 n. 3, 2017, pp. 607–616. [18] M. Ma, X. Liu, C. Zhang, LFC for multi-area interconnected power
system concerning wind turbines based on DMPC, IET Gener.
Transm. Distrib., vol. 11 n. 10, 2017, pp. 2689–2696. [19] H. Wang, Z. Chen, Q. Jiang, Optimal control method for wind farm
to support temporary primary frequency control with minimized
wind energy cost, IET Renew. Power Gener., vol. 9 n. 4, 2015, pp.
350 –359. [20] Global Wind Energy Council (GWEC), Global wind report annual
market update 2018, available at http://www.gwec.net [21] Global market outlook for photovoltaics 2014-2018, available at
http://www.pvresources.com [22] Egyptian Electricity Holding Company, Annual Report of
2014-2015, available at http://www.eehc.gov.eg/eehcportal/YearlyReports.aspx
[23] G. Magdy, A. Bakeer, G. Shabib, A.A. Elbaset, Y. Mitani,
Decentralized Model Predictive Control Strategy of a Realistic
Multi Power System Automatic Generation Control, IEEE Conf.,
16th International Middle East Power Systems Conference
G. Magdy et al.
Copyright © 2018 Praise Worthy Prize S.r.l. - All rights reserved International Journal on Energy Conversion, Vol. 6, N. 1
31
(MEPCON’19) ~ December 19-21, 2017, Menoufia University,
Egypt, pp. 190-196. [24] The Wind Power, Wind energy database, available at
https://www.thewindpower.net/turbine_en_42_gamesa_g52-850.p hp
[25] J. Kennedy, R. Eberhart, Particle Swarm Optimization, IEEE
International Conference on Neural Networks, Perth, Australia,
vol. 4 n. 1, 1995, pp. 1942-1948. [26] T. Kerdphol, Y. Qudaih, Y. Mitani, Optimum battery energy
storage system using PSO considering dynamic demand for
microgrids, Electrical Power and Energy Systems, vol. 83 n. 1,
2016, pp. 58-66. [27] G. Magdy, A. Bakeer, G. Shabib, A.A. Elbaset, Y. Mitani, Discrete-
time optimal controller for load frequency control of multi-source
power system in Egypt, IEEE Conf., Innovative Trends in Computer
Engineering (ITCE)~February 19-21, 2018, Aswan, Egypt. [28] J. Nanda, S. Mishra and L. C. Saikia, Maiden Application of
Bacterial Foraging-Based Optimization Technique in Multiarea
Automatic Generation Control, IEEE Trans. on Power system, vol.
24 n. 2, 2009, pp. 602-609. [29] European Network of Transmission System Operators for
Electricity (entsoe), Supporting Document for the Network Code on
Load-Frequency Control and Reserves, chapter 4, 2013, pp. 17-36. [30] Hasanien, H., Muyeen, S., Benbouzid, M., Optimal Control of a
One-Area LFC System Including Time Delay Using Shuffled Frog
Leaping Algorithm, (2017) International Journal on Energy
Conversion (IRECON), 5 (5), pp. 130-134.
Authors’ information 1 Department of
Electrical and Electronics Engineering, Kyushu Institute of Technology
(KIT), Fukuoka, 804-8550, Japan. E-mail: [email protected] 2 Department of Electrical Engineering, Faculty of Energy Engineering,
Aswan University, Aswan 81528, Egypt. 3 Higher Institute of Engineering and technology, King Marriott,
Alexandria 23713, Egypt. 4 Department of Electrical Engineering, Faculty of Engineering, Minia
University, Minia 61517, Egypt. 5 Electrical Engineering Department, American University, Madaba,
Jordan. 6 Electrical and Computer Engineering Department, University of
Kurdistan, Sanandaj, Iran. G. Magdy was born in Qena, Egypt, in 1989.
He received the B.Sc. and M.Sc. degrees in
electrical engineering at the Faculty of Energy
Engineering, Aswan University, Egypt, in 2011
and 2014, respectively, and is currently
pursuing the Ph.D. degree in the Department of
Electrical and Electronics Engineering,
Graduate School of Engineering, Kyushu Institute of Technology, Kitakyushu, Japan. In
2012, he joined the Department of Electrical Engineering, Faculty of
Energy Engineering, Aswan University, Aswan, Egypt, first as
demonstrator and then becoming Assistant lecturer in 2014. His research
interests include power system stability, advanced control of power
system and digital control techniques, all applied to power systems in
addition to renewable energy applications.
G. Shabib received his B.Sc. degree in
electrical engineering from Al Azhar
University. In October 1982, he joined the
electrical engineering department, King Fahad
University of Petroleum and Minerals, Dhahran
Saudi Arabia as research assistant. In
December 1985, he received his M.Sc. degree
in electrical engineering at King Fahad
University of Petroleum and Minerals. In November 1987, he joined
Qassim Royal Institute, Qassim, Saudi Arabia as lecturer. He received his
Ph.D. degree from Menoufia University, Egypt, in 2001. He joined
Aswan High Institute of Energy, South Valley University, Aswan, Egypt
in 1999. He joined Digital Control Laboratory, Tsukuba University, Japan
as visiting Professor in 2006–2007. His research interests are power
system stability, control, Self-tuning control, Fuzzy logic techniques,
digital control techniques, all as applied to power systems.
Adel A. Elbaset was born in Nag Hamadi,
Qena-Egypt, on October 24, 1971. He received
the B.Sc., M.Sc., and Ph.D. at the Faculty of
Engineering, Department of Electrical
Engineering, Minia University, Egypt, in 1995,
2000 and 2006, respectively. He is a staff
member of the Faculty of Engineering, Electrical Engineering Dept., Minia
University, Egypt. He was visiting assistant professor at Kumamoto
University, Japan, until August 2009. Presently, he is Professor at the
Department of Electrical Engineering. His research interests are in the
area of power electronics, power system, neural network, fuzzy systems
and renewable energy and Optimization.
Thongchart Kerdphol received his B.Eng.
and M.Eng at Kasetsart University, Bangkok,
Thailand, in 2010 and 2012. Currently, he is a
Ph.D. student sponsored by the Japan Student
Services Organization (JASSO honors
scholarship) and Kyushu Institute of
Technology (100th Anniversary scholarship) at
Mitani laboratory, KIT. His research interests
are in the area of analysis and design of power systems, dynamics and controls.
Yaser Qudaih Graduated from the University
of Engineering and Technology, Lahore,
Pakistan, in 1996, as an electrical engineer. He
Completed his M.Sc. and Ph.D. at Kumamoto
University, Japan, in Electrical Engineering. He
worked as Project Assistant Professor at the
Department of Electrical Engineering and
Electronics, Kyushu Institute of Technology (KIT), Japan. Currently, he is assistant professor at the American University of Madaba (AUM) in
Jordan and the chair of the Innovation program at AUM. His areas of
interest include renewable energy power systems and Smart Grid
Applications. He is a member of the Institute of Electrical Engineers of
Japan and of IEEE.
-
32
G. Magdy et al.
Hassan Bevrani (S’90–M’04–SM’08) received
the Ph.D. degree in electrical engineering at
Osaka University, Osaka, Japan, in 2004. From
2004 to 2014, he has worked as Postdoctoral
Fellow, Senior Research Fellow, Visiting
Professor and Professor at
Kumamoto University, Japan,
Queensland University of Technology, Australia, Kyushu Institute of Technology, Japan, Ecole
Centrale de Lille, France and Osaka University. He is currently working
as Professor at the University of Kurdistan, Iran. He has authored five
books, ten book chapters, and over 200 journal/conference papers. His
current research interests include smart grid control, robust power system
monitoring and control and microgrid dynamics and control.
Yasunori Mitani received B.Sc., M.Sc., and
D.Eng. degrees in Electrical Engineering at
Osaka University, Japan in 1981, 1983 and
1986, respectively. He was visiting research
associate at the University of California,
Berkeley, from 1994 to 1995. He is currently
professor at the department of electrical and
electronics engineering, Kyushu Institute of
Technology (KIT), Japan. At present, he is the head of environmental
management center of KIT and vice dean of a graduate school of
engineering, KIT.
-
33
Copyright © 2018 Praise Worthy Prize S.r.l. All rights reserved International Journal on Energy Conversion, Vol. 6, N. 1